Abstract
Emotion detection is an area of sentiment analysis that focuses on the extraction and evaluation of feelings. Many deep learning and machine learning researchers have found emotional content in text. People’s lives all across the world were significantly impacted by the COVID-19 pandemic. The social networking site twitter were very helpful in documenting people’s emotion and views. In literature there are two methods that has been used extensively for the emotion identification; one is Multinomial Naive Bayes model, and another is Bidirectional Long Short-Term Memory (LSTM). In this research, we have identified the efficient approach among the two mentioned approaches. We have compared the multinomial naive bayes model and bidirectional LSTM for identifying emotion. To identify the emotion in text, the tweet from twitter is utilized; that contains emotions in the form of text. The results show that bidirectional Long-Short Term Memory approach outperforms as compared to the multinomial naïve bayes approach (MNB). The bidirectional LSTM approach enhances the emotion detection over the MNB approach. The time taken for the training of the tweets differs for the two approaches. Due to its longer training period, the multinomial naive bayes technique may not be suitable for use with huge datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rajput, N., Ahuja, B., Rathi, V.: Word frequency and sentiment analysis of Twitter messages during Coronavirus pandemic (2020). https://doi.org/10.48550/arXiv.2004.03925
Mansoor, M., Gurumurthy, K., Anantharam, U., Prasad, V.: Global sentiment analysis of COVID-19 tweets over time (2020)
Suneera, C., Prakash, J.: Performance analysis of machine learning and deep learning models for text classification, pp. 1–6 (2020). https://doi.org/10.1109/INDICON49873.2020.9342208
Tareq, A., Hewahi, N.: Sentiment analysis of tweets during COVID-19 pandemic using BLSTM. In: 2021 International Conference on Data Analytics for Business and Industry (ICDABI), Sakheer, Bahrain, pp. 245–249 (2021). https://doi.org/10.1109/ICDABI53623.2021.9655932
Vijay, T., Chawla, A., Dhanka, B., Karmakar, P.: Sentiment analysis on COVID-19 Twitter data. In: 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), Jaipur, India, pp. 1–7 (2020). https://doi.org/10.1109/ICRAIE51050.2020.9358301
Alawneh, L., Mohsen, B., Al-Zinati, M., Shatnawi, A., Al-Ayyoub, M.: A comparison of unidirectional and bidirectional LSTM networks for human activity recognition. In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Austin, TX, USA, pp. 1–6 (2020). https://doi.org/10.1109/PerComWorkshops48775.2020.9156264
Sumanathilaka, T.G.D.K., Selvarai, V., Raj, U., Raiu, V.P., Prakash, J.: Emotion detection using bi-directional LSTM with an effective text pre-processing method. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, pp. 1–4 (2021). https://doi.org/10.1109/ICCCNT51525.2021.9579844
Xu, G., Meng, Y., Qiu, X., Yu, Z., Wu, X.: Sentiment analysis of comment texts based on BiLSTM. IEEE Access 7, 51522–51532 (2019). https://doi.org/10.1109/ACCESS.2019.2909919
Sushmitha, M., Suresh, K., Vandana, K.: To predict customer sentimental behavior by using enhanced Bi-LSTM technique. In: 2022 7th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, pp. 969–975 (2022). https://doi.org/10.1109/ICCES54183.2022.9835947
Abbas, M., Ali, K., Memon, S., Jamali, A., Memon, S., Ahmed, A.: Multinomial Naive Bayes classification model for sentiment analysis (2019). https://doi.org/10.13140/RG.2.2.30021.40169
Sharupa, N.A., Rahman, M., Alvi, N., Raihan, M., Islam, A., Raihan, T.: Emotion detection of twitter post using multinomial Naive Bayes. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India, pp. 1–6 (2020). https://doi.org/10.1109/ICCCNT49239.2020.9225432
Helmi Setyawan, M.Y., Awangga, R.M., Efendi, S.R.: Comparison of multinomial Naive Bayes algorithm and logistic regression for intent classification in chatbot. In: 2018 International Conference on Applied Engineering (ICAE), Batam, Indonesia, pp. 1–5 (2018). https://doi.org/10.1109/INCAE.2018.8579372
Jianqiang, Z., Xiaolin, G., Xuejun, Z.: Deep convolution neural networks for Twitter sentiment analysis. IEEE Access 6, 23253–23260 (2018). https://doi.org/10.1109/ACCESS.2017.2776930
Chirawichitchai, N.: Sentiment classification by a hybrid method of greedy search and multinomial Naïve Bayes algorithm. In: 2013 Eleventh International Conference on ICT and Knowledge Engineering, Bangkok, Thailand, pp. 1–4 (2013). https://doi.org/10.1109/ICTKE.2013.6756285
Guo, J.: Deep learning approach to text analysis for human emotion detection from big data. J. Intell. Syst. 31, 113–126 (2022). https://doi.org/10.1515/jisys-2022-0001
Hasan, M., Rundensteiner, E., Agu, E.: Automatic emotion detection in text streams by analyzing Twitter data. Int. J. Data Sci. Anal. 7, 35–51 (2019). https://doi.org/10.1007/s41060-018-0096-z
https://www.gabormelli.com/RKB/Bidirectional_LSTM_%28BiLSTM%29_Training_Task. Bidirectional LSTM (BiLSTM) Training Task - GM-RKB, 2 May 2023
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lakshitha, S.K., Naga Pranava Shashank, V., Richa, Gupta, S. (2024). A Comparison of Multinomial Naïve Bayes and Bidirectional LSTM for Emotion Detection. In: Aurelia, S., J., C., Immanuel, A., Mani, J., Padmanabha, V. (eds) Computational Sciences and Sustainable Technologies. ICCSST 2023. Communications in Computer and Information Science, vol 1973. Springer, Cham. https://doi.org/10.1007/978-3-031-50993-3_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-50993-3_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50992-6
Online ISBN: 978-3-031-50993-3
eBook Packages: Computer ScienceComputer Science (R0)